Overview

Dataset statistics

Number of variables14
Number of observations8760
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory958.3 KiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric10
Categorical3

Alerts

Dew point temperature(C) is highly overall correlated with Humidity(%) and 2 other fieldsHigh correlation
Humidity(%) is highly overall correlated with Dew point temperature(C)High correlation
Rented Bike Count is highly overall correlated with Temperature(C)High correlation
Seasons is highly overall correlated with Dew point temperature(C) and 1 other fieldsHigh correlation
Temperature(C) is highly overall correlated with Dew point temperature(C) and 2 other fieldsHigh correlation
Holiday is highly imbalanced (71.7%)Imbalance
Functioning Day is highly imbalanced (78.7%)Imbalance
Rented Bike Count has 295 (3.4%) zerosZeros
Hour has 365 (4.2%) zerosZeros
Solar Radiation (MJ/m2) has 4300 (49.1%) zerosZeros
Rainfall(mm) has 8232 (94.0%) zerosZeros
Snowfall (cm) has 8317 (94.9%) zerosZeros

Reproduction

Analysis started2026-02-19 18:55:18.323771
Analysis finished2026-02-19 18:55:32.252521
Duration13.93 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Date
Date

Distinct365
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Minimum2017-12-01 00:00:00
Maximum2018-11-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-19T18:55:32.357063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:32.488579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rented Bike Count
Real number (ℝ)

High correlation  Zeros 

Distinct2166
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean704.60205
Minimum0
Maximum3556
Zeros295
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:32.620975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q1191
median504.5
Q31065.25
95-th percentile2043
Maximum3556
Range3556
Interquartile range (IQR)874.25

Descriptive statistics

Standard deviation644.99747
Coefficient of variation (CV)0.91540674
Kurtosis0.85338699
Mean704.60205
Median Absolute Deviation (MAD)373.5
Skewness1.1534282
Sum6172314
Variance416021.73
MonotonicityNot monotonic
2026-02-19T18:55:32.760218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0295
 
3.4%
12219
 
0.2%
22319
 
0.2%
26219
 
0.2%
16518
 
0.2%
10318
 
0.2%
18918
 
0.2%
17817
 
0.2%
17017
 
0.2%
7117
 
0.2%
Other values (2156)8303
94.8%
ValueCountFrequency (%)
0295
3.4%
23
 
< 0.1%
32
 
< 0.1%
45
 
0.1%
53
 
< 0.1%
63
 
< 0.1%
74
 
< 0.1%
87
 
0.1%
912
 
0.1%
107
 
0.1%
ValueCountFrequency (%)
35561
< 0.1%
34181
< 0.1%
34041
< 0.1%
33841
< 0.1%
33801
< 0.1%
33651
< 0.1%
33091
< 0.1%
32981
< 0.1%
32771
< 0.1%
32561
< 0.1%

Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros365
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:32.871028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9225817
Coefficient of variation (CV)0.60196363
Kurtosis-1.2041763
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum100740
Variance47.922137
MonotonicityNot monotonic
2026-02-19T18:55:32.968983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0365
 
4.2%
1365
 
4.2%
2365
 
4.2%
3365
 
4.2%
4365
 
4.2%
5365
 
4.2%
6365
 
4.2%
7365
 
4.2%
8365
 
4.2%
9365
 
4.2%
Other values (14)5110
58.3%
ValueCountFrequency (%)
0365
4.2%
1365
4.2%
2365
4.2%
3365
4.2%
4365
4.2%
5365
4.2%
6365
4.2%
7365
4.2%
8365
4.2%
9365
4.2%
ValueCountFrequency (%)
23365
4.2%
22365
4.2%
21365
4.2%
20365
4.2%
19365
4.2%
18365
4.2%
17365
4.2%
16365
4.2%
15365
4.2%
14365
4.2%

Temperature(C)
Real number (ℝ)

High correlation 

Distinct546
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.882922
Minimum-17.8
Maximum39.4
Zeros21
Zeros (%)0.2%
Negative1433
Negative (%)16.4%
Memory size68.6 KiB
2026-02-19T18:55:33.082708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-17.8
5-th percentile-7.1
Q13.5
median13.7
Q322.5
95-th percentile30.7
Maximum39.4
Range57.2
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.944825
Coefficient of variation (CV)0.92718289
Kurtosis-0.83778629
Mean12.882922
Median Absolute Deviation (MAD)9.4
Skewness-0.19832553
Sum112854.4
Variance142.67885
MonotonicityNot monotonic
2026-02-19T18:55:33.237151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.540
 
0.5%
19.140
 
0.5%
23.439
 
0.4%
20.738
 
0.4%
7.638
 
0.4%
24.237
 
0.4%
20.235
 
0.4%
19.434
 
0.4%
1934
 
0.4%
21.933
 
0.4%
Other values (536)8392
95.8%
ValueCountFrequency (%)
-17.81
 
< 0.1%
-17.52
 
< 0.1%
-17.41
 
< 0.1%
-16.91
 
< 0.1%
-16.51
 
< 0.1%
-16.42
 
< 0.1%
-16.23
< 0.1%
-16.12
 
< 0.1%
-162
 
< 0.1%
-15.95
0.1%
ValueCountFrequency (%)
39.41
 
< 0.1%
39.31
 
< 0.1%
391
 
< 0.1%
38.71
 
< 0.1%
381
 
< 0.1%
37.92
 
< 0.1%
37.83
< 0.1%
37.61
 
< 0.1%
37.51
 
< 0.1%
37.46
0.1%

Humidity(%)
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.226256
Minimum0
Maximum98
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:33.375576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q142
median57
Q374
95-th percentile94
Maximum98
Range98
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.362413
Coefficient of variation (CV)0.34971188
Kurtosis-0.80355919
Mean58.226256
Median Absolute Deviation (MAD)16
Skewness0.059578973
Sum510062
Variance414.62788
MonotonicityNot monotonic
2026-02-19T18:55:33.507435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97173
 
2.0%
53173
 
2.0%
43164
 
1.9%
57159
 
1.8%
56157
 
1.8%
47156
 
1.8%
51155
 
1.8%
63153
 
1.7%
54151
 
1.7%
52150
 
1.7%
Other values (80)7169
81.8%
ValueCountFrequency (%)
017
0.2%
101
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
133
 
< 0.1%
1416
0.2%
1517
0.2%
1615
0.2%
1721
0.2%
1815
0.2%
ValueCountFrequency (%)
9850
 
0.6%
97173
2.0%
96111
1.3%
9568
 
0.8%
9454
 
0.6%
9338
 
0.4%
9227
 
0.3%
9138
 
0.4%
9052
 
0.6%
8962
 
0.7%

Wind speed (m/s)
Real number (ℝ)

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7249087
Minimum0
Maximum7.4
Zeros74
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:33.634418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.9
median1.5
Q32.3
95-th percentile3.7
Maximum7.4
Range7.4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.0363
Coefficient of variation (CV)0.60078543
Kurtosis0.72717945
Mean1.7249087
Median Absolute Deviation (MAD)0.7
Skewness0.8909548
Sum15110.2
Variance1.0739177
MonotonicityNot monotonic
2026-02-19T18:55:33.759661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1420
 
4.8%
1.2403
 
4.6%
0.9388
 
4.4%
1388
 
4.4%
0.8385
 
4.4%
1.4355
 
4.1%
1.3344
 
3.9%
1.5343
 
3.9%
1.6332
 
3.8%
0.6321
 
3.7%
Other values (55)5081
58.0%
ValueCountFrequency (%)
074
 
0.8%
0.149
 
0.6%
0.286
 
1.0%
0.3158
1.8%
0.4186
2.1%
0.5258
2.9%
0.6321
3.7%
0.7313
3.6%
0.8385
4.4%
0.9388
4.4%
ValueCountFrequency (%)
7.41
 
< 0.1%
7.31
 
< 0.1%
7.21
 
< 0.1%
6.91
 
< 0.1%
6.71
 
< 0.1%
6.11
 
< 0.1%
62
< 0.1%
5.84
< 0.1%
5.71
 
< 0.1%
5.62
< 0.1%

Visibility (10m)
Real number (ℝ)

Distinct1789
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1436.8258
Minimum27
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:33.887373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile300
Q1940
median1698
Q32000
95-th percentile2000
Maximum2000
Range1973
Interquartile range (IQR)1060

Descriptive statistics

Standard deviation608.29871
Coefficient of variation (CV)0.42336288
Kurtosis-0.96198013
Mean1436.8258
Median Absolute Deviation (MAD)302
Skewness-0.70178645
Sum12586594
Variance370027.32
MonotonicityNot monotonic
2026-02-19T18:55:34.020038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20002245
 
25.6%
199534
 
0.4%
198928
 
0.3%
198528
 
0.3%
199928
 
0.3%
199627
 
0.3%
199226
 
0.3%
199825
 
0.3%
199023
 
0.3%
198123
 
0.3%
Other values (1779)6273
71.6%
ValueCountFrequency (%)
271
< 0.1%
331
< 0.1%
341
< 0.1%
381
< 0.1%
531
< 0.1%
541
< 0.1%
591
< 0.1%
631
< 0.1%
662
< 0.1%
701
< 0.1%
ValueCountFrequency (%)
20002245
25.6%
199928
 
0.3%
199825
 
0.3%
199722
 
0.3%
199627
 
0.3%
199534
 
0.4%
199418
 
0.2%
199313
 
0.1%
199226
 
0.3%
199114
 
0.2%

Dew point temperature(C)
Real number (ℝ)

High correlation 

Distinct556
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0738128
Minimum-30.6
Maximum27.2
Zeros60
Zeros (%)0.7%
Negative3138
Negative (%)35.8%
Memory size68.6 KiB
2026-02-19T18:55:34.164406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-30.6
5-th percentile-19.505
Q1-4.7
median5.1
Q314.8
95-th percentile22.405
Maximum27.2
Range57.8
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation13.060369
Coefficient of variation (CV)3.2059326
Kurtosis-0.75542951
Mean4.0738128
Median Absolute Deviation (MAD)9.7
Skewness-0.36729844
Sum35686.6
Variance170.57325
MonotonicityNot monotonic
2026-02-19T18:55:34.298956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060
 
0.7%
21.143
 
0.5%
14.340
 
0.5%
21.240
 
0.5%
8.939
 
0.4%
21.839
 
0.4%
21.338
 
0.4%
2.238
 
0.4%
20.237
 
0.4%
21.536
 
0.4%
Other values (546)8350
95.3%
ValueCountFrequency (%)
-30.61
< 0.1%
-30.51
< 0.1%
-29.81
< 0.1%
-29.71
< 0.1%
-29.62
< 0.1%
-29.51
< 0.1%
-29.21
< 0.1%
-29.11
< 0.1%
-292
< 0.1%
-28.92
< 0.1%
ValueCountFrequency (%)
27.21
 
< 0.1%
26.82
< 0.1%
26.61
 
< 0.1%
26.31
 
< 0.1%
26.13
< 0.1%
262
< 0.1%
25.91
 
< 0.1%
25.82
< 0.1%
25.71
 
< 0.1%
25.62
< 0.1%

Solar Radiation (MJ/m2)
Real number (ℝ)

Zeros 

Distinct345
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56911073
Minimum0
Maximum3.52
Zeros4300
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:34.428381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.93
95-th percentile2.56
Maximum3.52
Range3.52
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.86874624
Coefficient of variation (CV)1.5264977
Kurtosis1.126433
Mean0.56911073
Median Absolute Deviation (MAD)0.01
Skewness1.5040397
Sum4985.41
Variance0.75472003
MonotonicityNot monotonic
2026-02-19T18:55:34.555688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04300
49.1%
0.01128
 
1.5%
0.0282
 
0.9%
0.0369
 
0.8%
0.0661
 
0.7%
0.0554
 
0.6%
0.0447
 
0.5%
0.1144
 
0.5%
0.0737
 
0.4%
0.1636
 
0.4%
Other values (335)3902
44.5%
ValueCountFrequency (%)
04300
49.1%
0.01128
 
1.5%
0.0282
 
0.9%
0.0369
 
0.8%
0.0447
 
0.5%
0.0554
 
0.6%
0.0661
 
0.7%
0.0737
 
0.4%
0.0833
 
0.4%
0.0932
 
0.4%
ValueCountFrequency (%)
3.522
< 0.1%
3.491
 
< 0.1%
3.451
 
< 0.1%
3.441
 
< 0.1%
3.424
< 0.1%
3.412
< 0.1%
3.393
< 0.1%
3.381
 
< 0.1%
3.364
< 0.1%
3.351
 
< 0.1%

Rainfall(mm)
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14868721
Minimum0
Maximum35
Zeros8232
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:34.683286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.128193
Coefficient of variation (CV)7.5876932
Kurtosis284.9911
Mean0.14868721
Median Absolute Deviation (MAD)0
Skewness14.533232
Sum1302.5
Variance1.2728194
MonotonicityNot monotonic
2026-02-19T18:55:34.813313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08232
94.0%
0.5116
 
1.3%
166
 
0.8%
1.556
 
0.6%
0.146
 
0.5%
231
 
0.4%
2.523
 
0.3%
0.220
 
0.2%
3.518
 
0.2%
0.416
 
0.2%
Other values (51)136
 
1.6%
ValueCountFrequency (%)
08232
94.0%
0.146
 
0.5%
0.220
 
0.2%
0.39
 
0.1%
0.416
 
0.2%
0.5116
 
1.3%
0.71
 
< 0.1%
0.83
 
< 0.1%
0.93
 
< 0.1%
166
 
0.8%
ValueCountFrequency (%)
351
< 0.1%
29.51
< 0.1%
241
< 0.1%
21.51
< 0.1%
211
< 0.1%
191
< 0.1%
18.52
< 0.1%
182
< 0.1%
171
< 0.1%
161
< 0.1%

Snowfall (cm)
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075068493
Minimum0
Maximum8.8
Zeros8317
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2026-02-19T18:55:34.940757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum8.8
Range8.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43674618
Coefficient of variation (CV)5.8179692
Kurtosis93.803324
Mean0.075068493
Median Absolute Deviation (MAD)0
Skewness8.4408008
Sum657.6
Variance0.19074723
MonotonicityNot monotonic
2026-02-19T18:55:35.062315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08317
94.9%
0.342
 
0.5%
139
 
0.4%
0.534
 
0.4%
0.934
 
0.4%
0.731
 
0.4%
0.822
 
0.3%
222
 
0.3%
0.421
 
0.2%
1.619
 
0.2%
Other values (41)179
 
2.0%
ValueCountFrequency (%)
08317
94.9%
0.12
 
< 0.1%
0.215
 
0.2%
0.342
 
0.5%
0.421
 
0.2%
0.534
 
0.4%
0.615
 
0.2%
0.731
 
0.4%
0.822
 
0.3%
0.934
 
0.4%
ValueCountFrequency (%)
8.82
< 0.1%
7.11
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
5.11
 
< 0.1%
52
< 0.1%
4.82
< 0.1%
4.32
< 0.1%
4.21
 
< 0.1%
4.14
< 0.1%

Seasons
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.9 KiB
1
2208 
2
2208 
0
2184 
3
2160 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
12208
25.2%
22208
25.2%
02184
24.9%
32160
24.7%

Length

2026-02-19T18:55:35.179703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T18:55:35.273960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12208
25.2%
22208
25.2%
02184
24.9%
32160
24.7%

Most occurring characters

ValueCountFrequency (%)
12208
25.2%
22208
25.2%
02184
24.9%
32160
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12208
25.2%
22208
25.2%
02184
24.9%
32160
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12208
25.2%
22208
25.2%
02184
24.9%
32160
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12208
25.2%
22208
25.2%
02184
24.9%
32160
24.7%

Holiday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.9 KiB
1
8328 
0
 
432

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18328
95.1%
0432
 
4.9%

Length

2026-02-19T18:55:35.374267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T18:55:35.438575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18328
95.1%
0432
 
4.9%

Most occurring characters

ValueCountFrequency (%)
18328
95.1%
0432
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18328
95.1%
0432
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18328
95.1%
0432
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18328
95.1%
0432
 
4.9%

Functioning Day
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.9 KiB
1
8465 
0
 
295

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18465
96.6%
0295
 
3.4%

Length

2026-02-19T18:55:35.516597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T18:55:35.582422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18465
96.6%
0295
 
3.4%

Most occurring characters

ValueCountFrequency (%)
18465
96.6%
0295
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18465
96.6%
0295
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18465
96.6%
0295
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18465
96.6%
0295
 
3.4%

Interactions

2026-02-19T18:55:30.617446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.062897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:20.180423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.930280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.003264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.056803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:25.399221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.834772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.407424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.520480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.038443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.174332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:20.283679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.043381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.110822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.168427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:25.509221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.995466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.521537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.631322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.137865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.278195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.115239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.145838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.210231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.270054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:25.615153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:27.130210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.622566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.729890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.236145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.390334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.223665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.249469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.309953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.377793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:25.726966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:27.280256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.730265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.838351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.329450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.495890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.318559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.351002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.428972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.477767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:25.846999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:27.424764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.842024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.938456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.434537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.619595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.418303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.460392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.531209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.584437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.018017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:27.581609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.957012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:30.064262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.535693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.734672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.524005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.571400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.641089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.712045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.181958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:27.752469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.084886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:30.176476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.637394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.848943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.635146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.693324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.756608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.821425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.346711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:27.906316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.198595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:30.294745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.740285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:19.967345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.737019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.805406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.862292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:24.930130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.516977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.092906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.307985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:30.407556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:31.844532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:20.076419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:21.838862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:22.908400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:23.964009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:25.299990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:26.678971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:28.265019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:29.418660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T18:55:30.512948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-19T18:55:35.647443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Dew point temperature(C)Functioning DayHolidayHourHumidity(%)Rainfall(mm)Rented Bike CountSeasonsSnowfall (cm)Solar Radiation (MJ/m2)Temperature(C)Visibility (10m)Wind speed (m/s)
Dew point temperature(C)1.0000.2320.1180.0030.5210.2130.3740.618-0.2490.0940.912-0.129-0.128
Functioning Day0.2321.0000.0240.0000.0680.0000.2190.2580.0070.0200.1950.0330.000
Holiday0.1180.0241.0000.0000.0790.0000.0990.1170.0220.0000.1460.0760.047
Hour0.0030.0000.0001.000-0.251-0.0260.3890.000-0.0320.2090.1210.0940.307
Humidity(%)0.5210.0680.079-0.2511.0000.368-0.2210.1840.050-0.4380.154-0.483-0.355
Rainfall(mm)0.2130.0000.000-0.0260.3681.000-0.2820.0260.002-0.0910.072-0.232-0.052
Rented Bike Count0.3740.2190.0990.389-0.221-0.2821.0000.313-0.2210.3820.5650.1760.148
Seasons0.6180.2580.1170.0000.1840.0260.3131.0000.1510.1330.6420.1360.110
Snowfall (cm)-0.2490.0070.022-0.0320.0500.002-0.2210.1511.000-0.077-0.307-0.0740.029
Solar Radiation (MJ/m2)0.0940.0200.0000.209-0.438-0.0910.3820.133-0.0771.0000.3280.0490.363
Temperature(C)0.9120.1950.1460.1210.1540.0720.5650.642-0.3070.3281.0000.0460.011
Visibility (10m)-0.1290.0330.0760.094-0.483-0.2320.1760.136-0.0740.0490.0461.0000.154
Wind speed (m/s)-0.1280.0000.0470.307-0.355-0.0520.1480.1100.0290.3630.0110.1541.000

Missing values

2026-02-19T18:55:31.987652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-19T18:55:32.150463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateRented Bike CountHourTemperature(C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
02017-12-012540-5.2372.22000-17.60.000.00.0311
12017-12-012041-5.5380.82000-17.60.000.00.0311
22017-12-011732-6.0391.02000-17.70.000.00.0311
32017-12-011073-6.2400.92000-17.60.000.00.0311
42017-12-01784-6.0362.32000-18.60.000.00.0311
52017-12-011005-6.4371.52000-18.70.000.00.0311
62017-12-011816-6.6351.32000-19.50.000.00.0311
72017-12-014607-7.4380.92000-19.30.000.00.0311
82017-12-019308-7.6371.12000-19.80.010.00.0311
92017-12-014909-6.5270.51928-22.40.230.00.0311
DateRented Bike CountHourTemperature(C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
87502018-11-30761147.8202.22000-13.81.670.00.0011
87512018-11-30768157.0203.31994-14.41.210.00.0011
87522018-11-30837167.2231.51945-12.60.720.00.0011
87532018-11-301047176.0292.11877-10.70.230.00.0011
87542018-11-301384184.7341.91661-9.80.000.00.0011
87552018-11-301003194.2342.61894-10.30.000.00.0011
87562018-11-30764203.4372.32000-9.90.000.00.0011
87572018-11-30694212.6390.31968-9.90.000.00.0011
87582018-11-30712222.1411.01859-9.80.000.00.0011
87592018-11-30584231.9431.31909-9.30.000.00.0011